Inference Scaling
intermediateImproving AI model performance by adding compute at inference time rather than during training. Techniques include chain-of-thought reasoning, self-consistency, and extended "thinking" time as seen in models like o1 and o3.
Overview
Inference scaling represents a paradigm shift from "train once, deploy cheaply" to "spend more compute per query for better results." Models like OpenAI's o1 and o3 demonstrate that letting models "think longer" dramatically improves performance on complex tasks. Traditional scaling focused on training—more parameters, more data, more compute during training. Inference scaling invests compute when the model is actually used, enabling dynamic quality-cost tradeoffs. This approach is particularly powerful for reasoning tasks where step-by-step thinking, self-verification, and exploration of multiple solution paths improve accuracy.
Key Concepts
Test-Time Compute
Spending additional compute during inference to improve output quality.
Extended Thinking
Allowing models more "reasoning steps" before producing final answers.
Compute-Quality Tradeoff
Users can choose faster/cheaper or slower/better responses.